Multiple Discrete Endogenous Variables in Weakly-Separable Triangular Models

Sung Jae Jun; Joris Pinkse; Haiqing Xu; Neşe Yıldız

doi:10.3390/econometrics4010007

,

and

¹

CAPCP and Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA 16802, USA

²

Department of Economics, University of Texas at Austin, 78712 Austin, TX, USA

³

Department of Economics, University of Rochester, 222 Harkness Hall, Rochester, NY 14627, USA

^*

Author to whom correspondence should be addressed.

Econometrics2016, 4(1), 7;https://doi.org/10.3390/econometrics4010007

This article belongs to the Special Issue Discrete Choice Modeling

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Abstract

We consider a model in which an outcome depends on two discrete treatment variables, where one treatment is given before the other. We formulate a three-equation triangular system with weak separability conditions. Without assuming assignment is random, we establish the identification of an average structural function using two-step matching. We also consider decomposing the effect of the first treatment into direct and indirect effects, which are shown to be identified by the proposed methodology. We allow for both of the treatment variables to be non-binary and do not appeal to an identification-at-infinity argument.

Keywords:

nonparametric identification; discrete endogenous regressors; triangular models

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